A powerful command line testing framework in Python with setup modules, parallel execution, and file comparison capabilities.
This is a lightweight and extensible automated testing framework that supports defining test cases via JSON/YAML formats, providing complete test execution, result verification, and report generation capabilities. The framework is designed to provide standardized test management for command-line tools and scripts, with enterprise-grade parallel execution support and advanced file comparison features.
"python ./script.py"pip install cli-test-framework
Python >= 3.9
from src.runners.json_runner import JSONRunner
runner = JSONRunner(
config_file="path/to/test_cases.json",
workspace="/project/root"
)
success = runner.run_tests()
from src.runners.parallel_json_runner import ParallelJSONRunner
# Multi-threaded execution (recommended for I/O-intensive tests)
runner = ParallelJSONRunner(
config_file="path/to/test_cases.json",
workspace="/project/root",
max_workers=4, # Maximum concurrent workers
execution_mode="thread" # "thread" or "process"
)
success = runner.run_tests()
from cli_test_framework import JSONRunner, EnvironmentSetup
# Using built-in environment variable setup
runner = JSONRunner("test_cases.json")
env_setup = EnvironmentSetup({
"TEST_ENV": "development",
"API_URL": "http://localhost:8080"
})
runner.setup_manager.add_setup(env_setup)
success = runner.run_tests()
# Compare two text files
compare-files file1.txt file2.txt
# Compare JSON files with key-based comparison
compare-files data1.json data2.json --json-compare-mode key-based --json-key-field id
# Compare HDF5 files with specific options
compare-files data1.h5 data2.h5 --h5-table table1,table2 --h5-rtol 1e-6
# Compare binary files with similarity check
compare-files binary1.bin binary2.bin --similarity
{
"setup": {
"environment_variables": {
"TEST_ENV": "development",
"API_URL": "http://localhost:8080",
"DEBUG_MODE": "true"
}
},
"test_cases": [
{
"name": "Environment Variable Test",
"command": "python",
"args": ["-c", "import os; print(f'Environment: {os.environ.get(\"TEST_ENV\")}')"],
"expected": {
"return_code": 0,
"output_contains": ["Environment: development"]
}
},
{
"name": "File Comparison Test",
"command": "compare-files",
"args": ["file1.txt", "file2.txt", "--verbose"],
"expected": {
"return_code": 0,
"output_contains": ["Files are identical"],
"output_matches": [".*comparison completed.*"]
}
}
]
}
setup:
environment_variables:
TEST_ENV: "production"
DATABASE_URL: "sqlite:///test.db"
test_cases:
- name: Environment Test
command: python
args:
- "-c"
- "import os; print(f'DB: {os.environ.get(\"DATABASE_URL\")}')"
expected:
return_code: 0
output_contains:
- "DB: sqlite:///test.db"
- name: Directory Scan Test
command: ls
args:
- -l
- docs/
expected:
return_code: 0
output_matches: ".*\\.md$"
compare-files file1.txt file2.txt \
--start-line 10 \
--end-line 20 \
--encoding utf-8
compare-files data1.json data2.json \
--json-compare-mode key-based \
--json-key-field id,name
New Feature: HDF5 group path expansion! By default, when you specify a group path in --h5-table, the comparator will automatically expand and compare all datasets and subgroups within that path.
# Compare specific tables/groups with auto-expansion (default behavior)
compare-files data1.h5 data2.h5 \
--h5-table group1/subgroupA \
--h5-rtol 1e-5 \
--h5-atol 1e-8
# Disable auto-expansion to compare only the specified path itself
compare-files data1.h5 data2.h5 \
--h5-table group1 \
--h5-no-expand-path
# Use regex patterns (also supports auto-expansion)
compare-files data1.h5 data2.h5 \
--h5-table-regex "group1/.*" \
--h5-structure-only
# Use comma-separated table names with regex (New in 0.3.7)
compare-files data1.h5 data2.h5 \
--h5-table-regex "table1,table2,table3" \
--h5-rtol 1e-6
compare-files binary1.bin binary2.bin \
--similarity \
--chunk-size 16384
graph TD
A[Test Cases] --> B{Execution Mode}
B -->|Sequential| C[JSONRunner/YAMLRunner]
B -->|Parallel| D[ParallelRunner]
D --> E[ThreadPoolExecutor/ProcessPoolExecutor]
C --> F[Command Parser]
E --> F
F --> G[Path Resolver]
G --> H[Sub-process Execution]
H --> I[Assertion Engine]
I --> J[Thread-Safe Result Collection]
J --> K[Report Generator]
L[File Comparator] --> M[Text Comparator]
L --> N[JSON Comparator]
L --> O[HDF5 Comparator]
L --> P[Binary Comparator]
# Handles complex commands like "python ./script.py"
command_parts = case["command"].split()
if len(command_parts) > 1:
actual_command = resolve_command(command_parts[0]) # "python"
script_parts = resolve_paths(command_parts[1:]) # "./script.py" -> full path
final_command = f"{actual_command} {' '.join(script_parts)}"
def resolve_command(self, command: str) -> str:
system_commands = {
'echo', 'ping', 'python', 'node', 'java', 'docker', ...
}
if command in system_commands or Path(command).is_absolute():
return command
return str(self.workspace / command)
class ParallelRunner(BaseRunner):
def __init__(self, max_workers=None, execution_mode="thread"):
self.max_workers = max_workers or os.cpu_count()
self.execution_mode = execution_mode
self._results_lock = threading.Lock()
self._print_lock = threading.Lock()
# Quick performance test
python performance_test.py
# Unit tests for parallel functionality
python -m pytest tests/test_parallel_runner.py -v
try:
runner = ParallelJSONRunner(config_file="test_cases.json")
success = runner.run_tests()
if not success:
# Check failed tests
for detail in runner.results["details"]:
if detail["status"] == "failed":
print(f"Failed test: {detail['name']}")
print(f"Error: {detail['message']}")
except Exception as e:
print(f"Execution error: {e}")
# Fallback to sequential execution
runner.run_tests_sequential()
Choose Appropriate Concurrency:
import os
# For CPU-intensive tasks
max_workers = os.cpu_count()
# For I/O-intensive tasks
max_workers = os.cpu_count() * 2
Test Case Design:
Debugging:
# Enable verbose output for debugging
runner = ParallelJSONRunner(
config_file="test_cases.json",
max_workers=1, # Set to 1 for easier debugging
execution_mode="thread"
)
{
"test_cases": [
{
"name": "Python Version Check",
"command": "python --version",
"args": [],
"expected": {
"output_matches": "Python 3\\.[89]\\.",
"return_code": 0
}
},
{
"name": "File Processing Test",
"command": "python ./process_file.py",
"args": ["input.txt", "--output", "result.txt"],
"expected": {
"return_code": 0,
"output_contains": ["Processing completed"]
}
}
]
}
Test Results Summary:
Total Tests: 15
Passed: 15
Failed: 0
Performance Statistics:
Sequential execution time: 12.45 seconds
Parallel execution time: 3.21 seconds
Speedup ratio: 3.88x
Detailed Results:
✓ Python Version Check
✓ File Processing Test
✓ JSON Comparison Test
...
Process Mode Serialization Error
Path Resolution Error
PathResolver system command listPerformance Not Improved
Command Not Found Error
"python ./script.py" not parsed correctly# Enable detailed logging
import logging
logging.basicConfig(level=logging.DEBUG)
# Check detailed results
import json
print(json.dumps(runner.results, indent=2, ensure_ascii=False))
class XMLRunner(BaseRunner):
def load_test_cases(self):
import xml.etree.ElementTree as ET
# Parse XML structure and convert to TestCase objects
class CustomParallelRunner(ParallelRunner):
def custom_preprocessing(self):
# Add custom logic before test execution
pass
class CustomAssertions(Assertions):
@staticmethod
def performance_threshold(execution_time, max_time):
if execution_time > max_time:
raise AssertionError(f"Execution too slow: {execution_time}s > {max_time}s")
JSONRunner code| Test Scenario | Sequential | Parallel (Thread) | Parallel (Process) | Speedup |
|---|---|---|---|---|
| 10 I/O tests | 5.2s | 1.4s | 2.1s | 3.7x |
| 20 CPU tests | 12.8s | 8.9s | 6.2s | 2.1x |
| Mixed tests | 8.5s | 2.3s | 3.1s | 3.7x |
python -m pytest tests/ -vThis project is licensed under the MIT License - see the LICENSE file for details.
For comprehensive documentation including detailed Setup Module guide, API reference, and advanced usage examples, see:
The user manual includes:
🚀 Ready to supercharge your testing workflow with setup modules, parallel execution and advanced file comparison!
For detailed parallel testing guide, see: PARALLEL_TESTING_GUIDE.md
你可以通过 --h5-data-filter 选项只比较满足特定条件的数据。例如：
# 只比较大于 1e-6 的数据
compare-files data1.h5 data2.h5 --h5-data-filter '>1e-6'
# 只比较绝对值大于 1e-6 的数据
compare-files data1.h5 data2.h5 --h5-data-filter 'abs>1e-6'
# 只比较小于等于 0.01 的数据
compare-files data1.h5 data2.h5 --h5-data-filter '<=0.01'
支持的表达式包括：>, >=, <, <=, ==，以及 abs 前缀（绝对值过滤）。
--h5-table-regex=table1,table2 now correctly matches both table1 and table2 instead of treating the entire string as a single regex pattern--h5-table-regex parameter--h5-data-filter option to compare only data meeting specific criteriaFAQs
A powerful command line testing framework in Python with setup modules, parallel execution, and file comparison capabilities.
We found that cli-test-framework demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Popular npm packages like eslint-config-prettier were compromised after a phishing attack stole a maintainer’s token, spreading malicious updates.
Security News
/Research
A phishing attack targeted developers using a typosquatted npm domain (npnjs.com) to steal credentials via fake login pages - watch out for similar scams.
Security News
Knip hits 500 releases with v5.62.0, refining TypeScript config detection and updating plugins as monthly npm downloads approach 12M.